The Predictioneer’s Game: Using the Logic of Brazen Self-Interest to See and Shape the Future

8

HOW TO PREDICT THE UNPREDICTABLE

THIS CHAPTER IS about the limitations of my models, some of my worst-ever predictions, and some of the potential dangers that can conceivably stem from “predictioneering.” Many a critic of mine will have well-worn and dog-eared pages in this stretch of the book!

My worst-ever prediction came in the months after Bill Clinton’s election to the presidency. When he was elected, it was obvious to everyone that he was going to try to push through a comprehensive health care plan. He assigned his wife to head a task force charged with designing a health program. At the time, I was engaged by a major brokerage firm to help work out what was likely to get through Congress so that they could design investment opportunities around the new program. As we all know now, the task force created a lot of heat, but no agreement on a new health care program. Instead, it failed dismally.

As it happened, my analysis of what the health care plan would look like led to one of my worst-ever predictions. Each and every detail of what came out of my analysis was both wrong and filled with lessons that improved future assessments. Models fail for three main reasons: the logic fails to capture what actually goes on in people’s heads when they make choices; the information going into the model is wrong—garbage in, garbage out; or something outside the frame of reference of the model occurs to alter the situation, throwing it off course. The last of these is what happened to my health care analysis.

In early 1993, I predicted what was likely to get through Congress sometime in 1993 or 1994. In some sense, all three of the limitations I mentioned were involved and were subsequently addressed as part of my personal learning experience. But by and large, the main problem had to do with an unforeseen event that completely altered the setting in which health care was going to be shepherded through Congress. Of course, the whole point of prediction is to forecast the unforeseen. Anyone can predict that the sun will rise in the east and set in the west tomorrow. Still, there’s unforeseen and then there’s unforeseen. I think you’ll see what I mean when we go through what happened to health care, at least as I looked at it.

Although the experts who provided the data identified a great many components of a comprehensive health plan—including questions related to long-term care, proportion of the population covered, costs of drugs, distribution of the tax burden for health care across the federal and state governments, as well as employers’ costs, total spending on health care, and even questions related to ancillary care—that would get congressional approval, none did. As it happens, the model predicted that Daniel Rostenkowski, then an influential Illinois congressman and, crucially, chairman of the powerful House Ways and Means Committee, was the key to getting health care legislation through Congress. Mr. Rostenkowski, however, was indicted on seventeen felony counts of corruption in 1994 (and later convicted) based on investigations that reached their height during 1993, as the Clinton White House’s health care push began in earnest. Rostenkowski’s salience for health care plummeted, of course, first in anticipation of his indictment and then even more as he fought to salvage his reputation, maintain his leadership position in Congress, and keep himself out of prison. He failed on all counts, and my prediction, based on his effective efforts on behalf of health care, also failed. As a result, contrary to my expectations, nothing passed through Congress.

Rostenkowski’s indictment was a shattering shock to the situation as analyzed; I’ll explain why in a moment. The model assumed, incorrectly, that the underlying conditions would remain unaltered during the period of negotiation and bargaining over health care. My client was not terribly happy that I got everything wrong, and neither was I, but at least I had the benefit of learning an important lesson. It was little consolation to know that if I repeated my analyses after dropping Rostenkowski from the data set, I got everything right. Without Rostenkowski, the model showed that agreement would not be reached in the House of Representatives, and that meant there would be no comprehensive health care plan. But, of course, that was analysis done in hindsight, and that is no way to help a client. Needless to say, the client was not particularly understanding or forgiving and never asked me to do another piece of work—a great disappointment, because I would have welcomed the opportunity to prove to them the value of modeling, and to do so for free. But they didn’t bite, and who can blame them? They invested valuable time as well as money in my analysis, and they had absolutely nothing to show for it.

What did my study find and why did it find it? The Rostenkowski study, as I now think of it, had a long list of players that included several members of Congress, Hillary Clinton herself, health care expert advisers from nursing homes, AARP, pharmaceutical companies, employers of all shapes and sizes, and so forth. Many issues were relatively difficult to resolve within the model’s own logic, taking many rounds of negotiation, posturing, and information exchanges before settling on what looked like a stable outcome—that is, an outcome that could get through the House and Senate. It was evident that more compromise was needed than some key players were prepared to accept. It was also evident that the study had to involve at least two (and possibly as many as four) distinct phases.

The first phase, common in many analyses of legislative decisions, focused on the period of lobbying and jockeying for position. In this phase, all of the players with an interest in shaping the outcome are part of the analysis. That includes many stakeholders who would not have a place at the table when it came time for the House and Senate to vote and for the president to sign or veto whatever they sent up to him. Organizations like Blue Cross—Blue Shield or the AMA that were utterly opposed to the Clinton plan, or some labor union leaders and local government interests that were strongly in favor of the plan, are included in the lobbying phase along with the decision makers. Then, when the lobbying game ends (according to the model’s rules), the analysis moves to the next phase. Because of the pulls and tugs during the lobbying period, many players’ positions will have shifted. They will have responded to offers of compromise or to coercion or to the anticipation of such pressures. So at the end of that first game, the decision makers move on to the next phase, but not with their original positions on individual health care issues intact. They move on at whatever position the model predicts they will hold when the lobbying game ends.

The next phase then pits just the decision makers against each other. Gone are labor union leaders, the AMA, the media, the Blues, and local and state governments, and gone is Hillary Rodham Clinton. Sure, she had influence in the lobbying phase, but she didn’t get to vote in Congress. From the model’s perspective, whatever whispers there might have been between her and President Clinton ended with the lobbying phase. He, and others, had ample opportunity in the first phase to succumb to, adjust to, or resist her arguments.

The second phase predicted the passage of a comprehensive bill in both the House and the Senate. It also predicted that the bill that would come before President Clinton was one he could easily have signed, although it would have been much altered from the legislation sought by Hillary Clinton. So there was little need in this case to do a further analysis to work out the negotiations between the House and Senate leadership over the exact contents of the proposed legislation, and there was no need to do a detailed study of the risks of veto and the prospects of overriding a veto. It just wasn’t an issue.

The numbers having been crunched, four results popped out of the analysis as being crucial to understanding where health care reform was headed. First, Hillary Clinton was an unusual stakeholder, not because she was First Lady, but because she showed the characteristics of someone who was content to fail while sticking to her principles. Despite pressure on her from every side, she was nearly immovable on each and every one of the issues I examined. This is a characteristic that is rarely seen in democratic politics (although many perceived this as the bargaining approach, really a bullying approach, used by George W. Bush). Sure, I had seen a rigid adherence to positions before in other studies I had done. The late Nigerian general Sani Abacha (and I do not mean to compare Hillary Clinton or George W. Bush to him on any substantive grounds—just bargaining style back then) was an important focus of many studies I did. He hardly ever shifted position, but then he didn’t have to. He got to dictate outcomes. Hillary Clinton barely shifted positions either, but, from a practical standpoint, she needed to. All the evidence suggests that her time in the U.S. Senate after her husband’s presidency made her a shrewd judge of when to flex some muscle and when to muster some flexibility. That should serve her well in the world now, but back then the model said she only knew about flexing muscle.

In the language of the times, Hillary Clinton had a tin ear when it came to politics. Fair enough. She had never run for office and had not been a politician. But her rigid willingness to go down in a seeming blaze of glory, but inevitably down nevertheless, hurt the chances of forging compromises with those who saw themselves excluded from the debate. This includes such important interest groups as the American Medical Association, much of the pharmaceutical industry, and others from whom even grudging support would have made selling health care reform much easier. Indeed, the analysis suggested that given the right response from the Clinton health care task force, the AMA was more flexible on many health care issues than was commonly perceived at the time. They could have been brought around to support a program that could have passed the House and Senate.

The second striking result was Bill Clinton’s bargaining style within the model’s logic. There are two ways to maneuver into a winning position. One is to persuade others to adopt your point of view. The other is to adopt theirs. Bill Clinton—in the model’s logic; I don’t know what he was actually doing behind closed doors—was the latter type. This was probably due to the modest degree of salience coupled with a fairly centrist, even slightly right position assigned to him on most health care issues by the expert panel I used to create the inputs for the model.

As the model saw things, President Clinton would sniff out where the strongest coalition was, and he would move close to it. He was like a person with a wet finger stuck in the wind to see which way the breeze is blowing. If Hillary Clinton’s principle at the time can be described as “Back what you believe in, come hell or high water,” Bill Clinton’s principle was “Win, no matter what constitutes winning.” With the benefit of hindsight, writing this a decade and a half later, I think it fits pretty well with what many have come to think of as Bill Clinton’s governing style.

The third striking result was how ineffective many members of Hillary Clinton’s task force were at looking past their personal beliefs. They were more open to compromise than Hillary Clinton, but they were reluctant to take her on, and so they were willing to accommodate the opposition too little to build a bridgehead to victory. In the model’s vocabulary, they gave in to her while failing to realize that they had more potential to change her mind than they thought.

And the fourth result, the really striking result, was that Dan Rostenkowski—that is, the person controlling the purse strings in the purse-string-shaping House Ways and Means Committee—had none of these limitations. He maneuvered skillfully—again, I am talking in terms of the model’s predictions; I don’t know what really went on, I only know how it turned out. He knew how to alter the thinking of other players from Congress. He knew how to reshape the president’s thinking and the perspective of many on the task force. He also handled the opposition lobbyists and interest groups skillfully.

What did the model see in Rostenkowski that was absent from the Clintons or other players? In order for health care reform to matter, it had to be funded, of course, and that was the province in which Dan Rostenkowski exerted the greatest influence. The expert data, not surprisingly, rated him as enormously powerful on questions related to how to pay for health care reform. And he was moderately conservative on this question, wanting to shift most of the cost away from the federal budget. Bill Clinton was perceived to argue for an even more conservative position when it came to paying for health care. So Rostenkowski was seen as the more moderate of the two, and he was believed to be as powerful as the president on this question.

Rostenkowski was positioned at a point that had a great mountain of powerful support behind it, he had enough salience to influence people (but not so much as to come across as excessively intense and committed), and he was surrounded by small, fragmented clusters of influence scattered across many positions with relatively little clout to withstand his pressure. In that environment (according to the model’s logic), Rostenkowski was positioned as a leader who could and would move people to his position. He found the right arguments and the right opportunities and the right targets to cajole or coerce so that the prospective winning position was located close to where he wanted it to be. He didn’t go to the winning position, he brought it to him. Thus, on one health care issue after another, because he exerted so much control over the money, what Rostenkowski wanted, Rostenkowski could pretty much get. Except, ah, except for those seventeen felony counts. They were not part of my analysis, they were truly unforeseen, and they made all of the difference. The felony counts were exogenous shocks—that is, the product of outside, unexamined forces unrelated to health care issues.

The political world and the business world are vulnerable to unanticipated shocks. With the Rostenkowski experience in hand, I realized I needed to have a way to anticipate unpredictable events so that I could take them into account. But how can you predict the unpredictable? Well, although it is impossible to anticipate unpredictable developments, it is possible to predict how big an “earthquake” is needed to disrupt a prediction. I worked out how to predict the magnitude of disruptions even if I could not know their exact source. We’ll look at how I have since incorporated this element into my work.

How I got to the ever-evolving solution is an interesting story in itself. Around the time that Dan Rostenkowski’s troubles led me to think about random shocks, John Lewis Gaddis, a world-renowned historian, now at Yale University but then at the University of Ohio in Athens, Ohio, invited me to spend a week with him and his students. Gaddis had written a paper in 1992 claiming that international relations theory was a failure because it didn’t predict the 1991 Gulf War, the demise of the Soviet Union, or the end of the cold war. Two well-known political scientists, Bruce Russett at Yale University and James Ray at Vanderbilt University, responded that Gaddis had not taken my predictive rational-choice work into account.¹ They contended that this body of work warranted being viewed as a rigorous scientific theory rather than just some exercise in fitting data to outcomes after they were known.

Gaddis paid attention to the claim by Russett and Ray that he had overlooked a relevant body of research. That is what led him to invite me to spend time with him and his students. He was a doubter, and he made no bones about that. Southern gentleman that he is, he couched his doubts in the most civil way; still, I was going to Athens, Ohio, with the expectation on John’s part that his students and he were going to expose my modeling as some sort of hocus-pocus.

I agreed to apply my method to any policy problem that Gaddis and his students agreed on, although I imposed two restrictions. First, they had to know enough about the issue they chose to be able to provide me with the data needed for the model, since I was unlikely to be an expert on the issue and anyway it would be best if the data came from doubters. Second, it had to be an issue for which the outcome would be known over a period ranging from a few months to a year or two, rather than something that would not be known for so long that they couldn’t judge in a timely fashion whether my model’s logic had gotten it right or not. The hope was to look at something we could then correspond about. They would know I had made predictions before the fact, and, the timing being right, they would be able to look back at what I had said and compare it to what actually happened later.

They chose to have me analyze what became the 1994 baseball strike. I made detailed predictions about such matters as whether there would be a strike (the model said yes), whether there would be a World Series that year (the model said no), and whether President Clinton’s eventual threatened intervention, which was predicted by the model, would end the strike (again, the prediction was no). I did an in-class interview with the two or three students who were “experts” on baseball, and then ran my computer model in front of all the students. I provided an analysis of the results on the spot. That way the students knew that nothing more went into my predictions than the data collected in class and the logic of my model. The predictions, as it happened, turned out to be correct.

Shortly before I left Athens, Professor Gaddis suggested that I write a paper applying the model to the end of the cold war. In particular, he proposed that I investigate whether the model would have correctly predicted the U.S. victory in the cold war based only on information that decision makers could have known shortly after World War II ended. That is, he asked for a sort of out-of-sample prediction of the type I used to validate the fraud model. And so my analytic experiences with Dan Rostenkowski and with the baseball strike came together to provide a motivation and a framework for assessing the end of the cold war. My work on this project, using only information available in 1948, would help me incorporate and test my new design for external shocks within a model, which, of course, I felt compelled to develop on account of my health care debacle.

I used Gaddis’s proposal and my awful experience with health care to think through how to predict the consequences of inherently unpredictable events. I put together a data set that my model could use to investigate alternative paths to the end—or continuation—of the cold war. The data on stakeholder positions were based on a measure of the degree to which each country in the world as of 1948 shared security interests with the United States or the Soviet Union. The procedure I used to evaluate shared interests was based on a method I developed in publications in the mid-1970s.² The procedure looks at how similar each pair of countries’ military alliance portfolios are to each other from year to year. Those who tended to ally with the same states in the same way were taken to share security concerns, and those who allied in significantly different ways (as the United States and the Soviet Union did) were taken to have different, perhaps opposed, security policies and interests.

The correlation of alliance patterns as of 1948 was combined with information on the relative clout or influence of each state in 1948. To asses clout I used a standard body of data developed by a project then housed at the University of Michigan called the Correlates of War Project. Those data, like my measure of security interests, can be downloaded by anyone who cares to. They are housed at a website called EUGene, designed by two political science professors who were interested in replicating some of my research on war.³

Each state in the data set—I focused on countries rather than individual decision makers to keep the data simple and easy to reproduce by others—was assigned a maximum salience score to reflect the urgency of security questions right after the Second World War. Combining these data to estimate expected gains and losses from shifting security policies, the model was run on these data for all country-pair combinations one hundred times. Each such run consisted of fifty “bargaining periods.” The “bargaining periods” were treated as years, and thus the model was being used to predict what would happen in the cold war roughly from 1948 until the end of the millennium.

Each country’s salience score was assigned a one-in-four chance of randomly changing each year. That seemed high enough to me to capture the pace at which a government’s attention might move markedly in one direction or another and not so high as to introduce more volatility than was likely within countries or across countries over relatively short intervals. Naturally, this could have been done with a higher or lower probability, so there is nothing more than a personal judgment behind the choice of a one-in-four chance of a “shock.”

Any changes in salience reflected hypothetical shifts in the degree to which security concerns dominated policy formation or the degree to which other issues, such as domestic matters, surfaced to shape decision making for this or that country. Thus, the salience data were “shocked” to capture the range and magnitude of possible political “earthquakes” that could have arisen after 1948. This was the innovation to my model that resulted from the combination of my visit to Ohio and my failed predictions regarding health care. Since then, I have incorporated ways to randomly alter not only salience but also the indicators for potential clout and for positions, and even for whether a stakeholder stays in the game or drops out, in a new model I am developing.

Neither the alliance-portfolio data used to measure the degree of shared foreign interests nor the influence data were updated to take real events after 1948 into account. The alliance-portfolio measure only changed in response to the model’s logic and its dynamics, given randomly shocked salience. Changes in the alliance correlations for all of the countries were the indicator of whether the Soviets or the Americans would prevail or whether they would remain locked in an ongoing struggle for supremacy in the world.

So here was an analysis designed to predict the unpredictable—that is, the ebb and flow of attentiveness to security policy as the premier issue in the politics of each state in my study. With enough repetitions (at the time, I did just a hundred, because computation took a very long time; today I would probably do a thousand or more) with randomly distributed shocks, we should have been able to see the range of possible developments on the security front. That, in turn, should have made it possible to predict the relative likelihood of three possible evolutions of the cold war: (a) it would end with a clear victory by the United States within the fifty-year period I simulated; (b) it would end with a clear victory by the Soviet Union in that same time period; or (c) it would continue, with neither the Soviet Union nor the United States in a position to declare victory.

What did I find? The model indicated that in 78 percent of the scenarios in which salience scores were randomly shocked, the United States won the cold war peacefully, sometimes by the early to mid-1950s, more often in periods corresponding to the late 1980s or early 1990s. In 11 percent of the simulations, the Soviets won the cold war, and in the remaining 11 percent, the cold war persisted beyond the time frame covered by my investigation. What I found, in short, was that the configuration of policy interests in 1948 already presaged an American victory over the Soviet Union. It was, as Gaddis put it, an emergent property. This was true even though the starting date, 1948, predated the formation of either NATO or the Warsaw Pact, each of which emerged in almost every simulation as the nations’ positions shifted from round to round according to the model’s logic.⁴

The selection of 1948 as the starting date was particularly challenging in that this was a time when there was concern that many countries in Western Europe would become socialist. This was a time, too, when many thought that a victory of communism over capitalism and authoritarianism over democracy was a historical inevitability. On the engineering front it was, of course, too late to change the course of events. Still, the model was quite provocative on this dimension, as it suggested opportunities that were passed up to win the cold war earlier. One of those opportunities, at the time of Stalin’s death (which, of course, was not a piece of information incorporated into the data that went into the model), was, as it turns out, contemplated by real decision makers at the time. They thought there might be a chance to wrest the Soviet Union’s Eastern European allies into the Western European fold. My model agreed. American decision makers did not pursue this possibility, because they feared it would lead to a war with the Soviet Union. My model disagreed, predicting that the Soviets in this period would be too preoccupied with domestic issues and would, undoubtedly with much regret, watch more or less helplessly as their Eastern European empire drifted away. We will, of course, never know who was right. We do know that that is what they did a few decades later, between 1989 and 1991.

So with the help of Dan Rostenkowski and John Gaddis’s students I was able to show how strongly the odds favored an American cold war victory. The account of the cold war, like the earlier examination of fraud, reminds us that prediction can look backward almost as fruitfully as it can look forward. Not everyone was as generous as John Gaddis in acknowledging that game-theory modeling might help sort out important issues, and not everyone should be (not that it isn’t nice when people are that generous). There should be and always will be critics.

There are plenty of good reasons for rejecting modeling efforts, or at least being skeptical of them, and plenty of bad reasons too. Along with technical failures within my models, or any models for that matter, there is the obvious limitation in that they are simply models, which are, of course, not reality. They are a simplified glance at reality. They can only be evaluated by a careful examination of what general propositions follow from their logic and an evaluation of how well reality corresponds with those propositions. Unfortunately, sometimes people look at lots of equations and think, “Real people cannot possibly make these complicated calculations, so obviously real people do not think this way.” I hear this argument just about every semester in one or another course that I teach. I always respond by saying that the opposite is true. Real people may not be able to do the cumbersome math that goes into a model, but that doesn’t mean they aren’t making much more complicated calculations in their heads even if they don’t know how to represent their analytic thought processes mathematically.

Try showing a tennis pro the equations that represent hitting a ball with topspin to the far corner of the opponent’s side of the court, making sure that the ball lands just barely inside the line and that it travels, say, at 90 miles an hour. Surely the tennis pro will look at the equations in utter bewilderment. Yet professional tennis players act as if they make these very calculations whenever they try to make the shot I just described. If the pro is a ranked player, then most of the time the shot is made successfully even though the decisions about arm speed, foot position, angle of the racket’s head, and so forth must be made in a fraction of a second and must be made while also working out the velocity, angle, and spin of the ball coming his or her way from across the court.

Since models are simplified representations of reality, they always have room for improvement. There is always a trade-off between adding complexity and keeping things manageable. Adding complexity is only warranted when the improvement in accuracy and reliability is greater than the cost of adding assumptions. This is, of course, the well-known principle of parsimony. I’ve made small and big improvements in my game-theory modeling over the years. My original forecasting model was static. It reported what would happen in one exchange of information on an issue. As such, it was a good forecaster but not much good at engineering. While I was tweaking that static model to improve its estimation of people’s willingness to take risks and to estimate their probability of prevailing or losing in head-to-head contests, I was also thinking about how to make the process dynamic. Real people, after all, are dynamic. They change their minds, they switch positions on questions, they make deals, and, of course, they bluff and renege on promises.

About ten years after creating the static version I finally worked out a dynamic model I was happy with. That is the model I’m mostly discussing in this book. Over the past few years I’ve been working on a completely new approach based on a more nuanced game than the one I described back in the third chapter. Preliminary tests of this new model indicate that it not only yields more accurate predictions, but also captures play dynamics more faithfully. As an added bonus, it also allows me to evaluate trade-offs across issues or across different dimensions on a single issue simultaneously. It also gives me the opportunity to assess how each player’s salience and influence changes from bargaining round to bargaining round, something the older model cannot do. I will apply this new model to some ongoing foreign policy crises and to global warming in the last two chapters. That will be my first foray into opening the opportunity to be embarrassed by my new approach.

The process of discovery is never-ending. That’s both the challenge and the excitement behind doing this kind of research: finding better and better ways to help people solve real problems through logic and evidence. Not everyone, though, shares my enthusiasm for this sort of effort at discovery.

Some critics object to predicting human behavior. They worry that government or corporations will misuse this knowledge. They’re concerned about the ethics of reducing people to equations. To me this is an odd set of objections, especially since it comes mostly from people who are unhappy with the quality of government policy choices and with corporate actions to begin with. Some of my academic colleagues particularly object to providing guidance to the intelligence community, the “evil” CIA, on national security matters. They seem to think that the government shouldn’t have the best tools at its disposal to make the best choices possible. I don’t share that view. If we want better decisions from our government, we ought to be willing to help it improve its decision making.

Yes, there is always a risk that any tool will be misused. But science is about understanding how the world works. Different people have different personal views about what will make the world a better place, and it’s the job of officials and citizens to regulate unethical uses of information. Further, it is the responsibility of each of us as individuals to withhold our expertise when we think its use will make the world, or our little part of it, a worse place.

I turn down clients when I don’t want to help them achieve their goals. Many years ago, for example, I was approached by someone claiming to represent the Libyan government. The person who contacted me wanted me to figure out how to facilitate overthrowing the Egyptian government then led by Anwar Sadat. The contact proposed flying me to Geneva, Switzerland, to avoid the possibility of the United States government or some other government being able to subpoena the results of my then very primitive modeling effort. I was offered a million dollars for my trouble. There is no way for me to know whether this approach was authentic or a hoax, although it certainly seemed real. I declined and immediately contacted people in the U.S. government to alert them to my experience.

Several years later I was contacted by yet another person with an unsavory proposal. This person represented himself as an agent for Mobutu Sese Seko of Zaire. Mobutu’s hold on power had become tenuous. His economy was doing poorly, his soldiers were becoming agitated, and his loyal followers were becoming shaky because he was known to have a terminal illness. They were presumably worried about who would protect them and take care of them financially when he was gone. The contact person wanted to know if I could work out how to salvage Mobutu’s control over Zaire and offered a success fee of 10 percent of Mobutu’s offshore financial holdings. I know this sounds hard to believe, but it happened, and it was before unscrupulous Nigerians had worked out their famous Internet bank scams.

Mobutu at the time was reputed to be worth somewhere between $6 billion and $20 billion. If this had been for real and if I could have engineered his continuation in office until he died peacefully or chose to step down, and if I had been willing to do so, I could have been paid an unbelievable fortune. But even if the fortune had been believable, the answer would still have been the same. As in the alleged Libya offer, I said no without a moment’s hesitation. I was confident that Mobutu’s difficulty was an analytic problem with a solution, but no amount of money could have justified my intervention. My main concern was that I would be on the radar screen of people I really preferred not to know about me. And once again I contacted people in the U.S. government to alert them to the situation.

Of course, my personal judgment about who to do business with might differ from someone else’s judgment. I couldn’t see any justification for helping anyone topple Sadat. Here was a man who had put his life at risk—and would tragically lose it—in a sincere and successful effort to advance the cause of peace. Mobutu’s case could be seen as (ever so slightly) more complicated. There was a slender ethical case to be made on Mobutu’s behalf. While it did not appeal to me, one could easily have argued that whoever came after Mobutu might be even worse. Back then, and even immediately after his overthrow, it wasn’t clear that the Congo was moving in a better direction. Still, for me the answer was unambiguous. For others—who knows how they would have evaluated the pros and cons of applying insights from science to help or hinder a dictator like Mobutu?

Some of you may think I should not use game theory to help big corporations get good settlements in litigation, especially when their opponents in civil matters may not be able to afford (or choose not to afford) comparable help. Others may think I don’t do enough to help plaintiffs (although my firm is happy to do so; we just aren’t asked very often), or what have you. Still others may subscribe to the lawyer’s dictum: Everyone is entitled to the best defense they can muster. We all have our individual standards about how to use or withhold our knowledge and skills, and that is as it should be.

In the end, I believe advances in scientific knowledge almost always better the human condition. If we turn ourselves into Luddites, we’ll just shift the advantages of knowledge to others. Remember, after Galileo’s persecution by the Catholic Church, physics went into decline in Italy for centuries, until, perhaps, the arrival of Enrico Fermi on the scene. Despite the setbacks in Italy, that didn’t mean research into physics stopped. It moved to Protestant northern Europe, leaving Italy to fall behind. Similarly, efforts to stymie science in China caused that country, once the world’s most advanced in scientific knowledge and discovery, to descend into scientific oblivion. China’s emperors chose to have their people look within themselves rather than at the stars; China is still struggling to overcome the deficit it created for itself. I hope we will not make the same mistake. As for me, I continue to look for ways to improve my understanding of how the world of strategic human behavior works. And that’s central to my motivation to continue learning from past failures.

As I said earlier, and as we’ve seen in this chapter, prediction can look backward almost as fruitfully as it can look forward, providing remarkable insight not only into what happened but also into what might have been. Accordingly, in the next chapter we’ll have some fun with history. We will look at how World Wars I and II might have been avoided, and how Sparta might have prevented its colossal collapse after its stunning victory in the Peloponnesian War. And while everyone knows Columbus sailed the ocean blue in fourteen hundred and ninety-two, what they don’t know is that his experience presents an interesting bargaining problem—one whose outcome explains why Spain said yes and why Portugal (among others) said no, forever changing the course of history. In looking at the past with a game-theory microscope we begin to grasp the logic behind the history we know (and a sense of just how un-inevitable history is) and, sometimes to tragic effect, the missed opportunities for strategic choices that would have altered its course.